Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors
Several decades of research have investigated the neural connections between stroke-induced brain damage and language difficulties. Typically, lesion-symptom mapping (LSM) studies that address this connection have relied on mass univariate statistics, which do not account for multidimensional relati...
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| Format: | Article |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Neuroimaging |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnimg.2025.1573816/full |
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| author | Deepa Tilwani Deepa Tilwani Deepa Tilwani Deepa Tilwani Christian O'Reilly Christian O'Reilly Christian O'Reilly Christian O'Reilly Nicholas Riccardi Valerie L. Shalin Valerie L. Shalin Dirk-Bart den Ouden Julius Fridriksson Svetlana V. Shinkareva Svetlana V. Shinkareva Amit P. Sheth Amit P. Sheth Amit P. Sheth Rutvik H. Desai Rutvik H. Desai |
| author_facet | Deepa Tilwani Deepa Tilwani Deepa Tilwani Deepa Tilwani Christian O'Reilly Christian O'Reilly Christian O'Reilly Christian O'Reilly Nicholas Riccardi Valerie L. Shalin Valerie L. Shalin Dirk-Bart den Ouden Julius Fridriksson Svetlana V. Shinkareva Svetlana V. Shinkareva Amit P. Sheth Amit P. Sheth Amit P. Sheth Rutvik H. Desai Rutvik H. Desai |
| author_sort | Deepa Tilwani |
| collection | DOAJ |
| description | Several decades of research have investigated the neural connections between stroke-induced brain damage and language difficulties. Typically, lesion-symptom mapping (LSM) studies that address this connection have relied on mass univariate statistics, which do not account for multidimensional relationships between variables. Machine learning (ML) techniques, which can capture these intricate connections, offer a promising complement to LSM methods. To test this promise, we benchmarked ML models on structural and functional MRI to predict aphasia severity (N = 238) and naming impairment (N = 191) for a cohort of chronic-stage stroke survivors. We used nested cross-validation to examine performance along three dimensions: (1) parcellation schemes (JHU, AAL, BRO, and AICHA atlases), (2) neuroimaging modalities (resting-state functional connectivity, structural connectivity, mean diffusivity, fractional anisotropy, and lesion location) and (3) ML methods (Random Forest, Support Vector Regression, Decision Tree, K Nearest Neighbors, and Gradient Boosting). The best results were obtained by combining the JHU atlas, lesion location, and the Random Forest model. This combination yielded moderate to high correlations with the two different behavioral scores. Key regions identified included several perisylvian areas and pathways within the language network. This work complements existing LSM methods with new tools for improving the prediction of language outcomes in stroke survivors. |
| format | Article |
| id | doaj-art-d260ca7340c14979b081cf7be59f13ea |
| institution | DOAJ |
| issn | 2813-1193 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroimaging |
| spelling | doaj-art-d260ca7340c14979b081cf7be59f13ea2025-08-20T03:12:39ZengFrontiers Media S.A.Frontiers in Neuroimaging2813-11932025-05-01410.3389/fnimg.2025.15738161573816Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivorsDeepa Tilwani0Deepa Tilwani1Deepa Tilwani2Deepa Tilwani3Christian O'Reilly4Christian O'Reilly5Christian O'Reilly6Christian O'Reilly7Nicholas Riccardi8Valerie L. Shalin9Valerie L. Shalin10Dirk-Bart den Ouden11Julius Fridriksson12Svetlana V. Shinkareva13Svetlana V. Shinkareva14Amit P. Sheth15Amit P. Sheth16Amit P. Sheth17Rutvik H. Desai18Rutvik H. Desai19Artificial Intelligence Institute, University of South Carolina, Columbia, SC, United StatesDepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC, United StatesCarolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC, United StatesInstitute for Mind and Brain, University of South Carolina, Columbia, SC, United StatesArtificial Intelligence Institute, University of South Carolina, Columbia, SC, United StatesDepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC, United StatesCarolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC, United StatesInstitute for Mind and Brain, University of South Carolina, Columbia, SC, United StatesDepartment of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, United StatesArtificial Intelligence Institute, University of South Carolina, Columbia, SC, United StatesDepartment of Psychology, Wright State University, Dayton, OH, United StatesDepartment of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, United StatesDepartment of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, United StatesInstitute for Mind and Brain, University of South Carolina, Columbia, SC, United StatesDepartment of Psychology, University of South Carolina, Columbia, SC, United StatesArtificial Intelligence Institute, University of South Carolina, Columbia, SC, United StatesDepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC, United StatesCarolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC, United StatesInstitute for Mind and Brain, University of South Carolina, Columbia, SC, United StatesDepartment of Psychology, University of South Carolina, Columbia, SC, United StatesSeveral decades of research have investigated the neural connections between stroke-induced brain damage and language difficulties. Typically, lesion-symptom mapping (LSM) studies that address this connection have relied on mass univariate statistics, which do not account for multidimensional relationships between variables. Machine learning (ML) techniques, which can capture these intricate connections, offer a promising complement to LSM methods. To test this promise, we benchmarked ML models on structural and functional MRI to predict aphasia severity (N = 238) and naming impairment (N = 191) for a cohort of chronic-stage stroke survivors. We used nested cross-validation to examine performance along three dimensions: (1) parcellation schemes (JHU, AAL, BRO, and AICHA atlases), (2) neuroimaging modalities (resting-state functional connectivity, structural connectivity, mean diffusivity, fractional anisotropy, and lesion location) and (3) ML methods (Random Forest, Support Vector Regression, Decision Tree, K Nearest Neighbors, and Gradient Boosting). The best results were obtained by combining the JHU atlas, lesion location, and the Random Forest model. This combination yielded moderate to high correlations with the two different behavioral scores. Key regions identified included several perisylvian areas and pathways within the language network. This work complements existing LSM methods with new tools for improving the prediction of language outcomes in stroke survivors.https://www.frontiersin.org/articles/10.3389/fnimg.2025.1573816/fullaphasialesion-symptom mappingneuroimagingmultivariate analysisstrokemachine learning |
| spellingShingle | Deepa Tilwani Deepa Tilwani Deepa Tilwani Deepa Tilwani Christian O'Reilly Christian O'Reilly Christian O'Reilly Christian O'Reilly Nicholas Riccardi Valerie L. Shalin Valerie L. Shalin Dirk-Bart den Ouden Julius Fridriksson Svetlana V. Shinkareva Svetlana V. Shinkareva Amit P. Sheth Amit P. Sheth Amit P. Sheth Rutvik H. Desai Rutvik H. Desai Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors Frontiers in Neuroimaging aphasia lesion-symptom mapping neuroimaging multivariate analysis stroke machine learning |
| title | Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors |
| title_full | Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors |
| title_fullStr | Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors |
| title_full_unstemmed | Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors |
| title_short | Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors |
| title_sort | benchmarking machine learning models in lesion symptom mapping for predicting language outcomes in stroke survivors |
| topic | aphasia lesion-symptom mapping neuroimaging multivariate analysis stroke machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fnimg.2025.1573816/full |
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